Intelligent Selection of Machining Parameters in Multi-pass Turnings Using a GA-based Approach

Optimization machining parameters are important in manufacturing world considering economic reason. To deal with this nonlinear optimization problem which aims to minimize the unit production cost (UC) in multi-pass turnings, this paper proposes a novel approach, which combines genetic algorithms (GAs) with a pass enumerating method. In the pass enumerating method, the number of all possible rough cuts is calculated in order to divide the whole complicated problem into several sub-problems. In applying GA to solve the problems, the bound adjustment of optimized variants method is used to represent the chromosome in order to reduce the number of infeasible individual during evolution. Computer simulation results show that the optimization approach can find the better results than other algorithms proposed previously to significantly reduce the UC.